Hi, I have a dataframe with multiple (appr. 20) columns containing vectors of different values (different distributions). Now I'd like to create a crosstable where I compare the distribution of each vector (df-column) with each other. For the comparison I want to use the ks.test(). The result should contain as row and column names the column names of the input dataframe and the cells should be populated with the p-value of the ks.test for each pairwise analysis.
My data.frame looks like: df <- data.frame(X=rnorm(1000,2),Y=rnorm(1000,1),Z=rnorm(1000,2)) And the test for one single case is: ks <- ks.test(df$X,df$Z) where the p value is: ks[2] How can I create an automatized way of this pairwise analysis? Any suggestions? I guess that is a quite common analysis (probably with other tests). cheers, Johannes ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
